May 1, 2024
Updated May 11, 2025
22 minute read
P-values are a fundamental concept in statistics, serving as a tool to help researchers and analysts assess the strength of evidence against a statistical hypothesis. At a high level, a p-value represents the probability of observing data as extreme as, or more extreme than, what was actually observed, assuming that a specific baseline assumption (called the null hypothesis) is true. Understanding p-values unlocks the ability to critically evaluate research findings, make data-driven decisions in various fields, and contribute to scientific and analytical discourse. For those intrigued by the power of data and the stories it can tell, or for individuals looking to enhance their analytical capabilities, delving into the world of p-values can be both an engaging and rewarding endeavor.
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Find a path to becoming a P-Values. Learn more at:
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Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
P-Values.
This rigorous textbook provides a comprehensive introduction to probability theory and statistical inference, covering topics such as random variables, distributions, and hypothesis testing.
This influential book provides a comprehensive treatment of Bayesian data analysis, including hierarchical models, Markov chain Monte Carlo methods, and applications in various fields.
Provides an accessible introduction to statistical methods used in psychology and offers coverage of advanced topics such as multiple regression and analysis of variance.
This classic book provides a thorough treatment of statistical power analysis, including methods for determining sample size and evaluating the power of statistical tests.
This concise and accessible book provides a comprehensive overview of statistical inference, including both classical and Bayesian approaches.
This practical guide covers statistical methods commonly used in medical research, including study design, data analysis, and interpretation of results.
This introductory textbook covers fundamental statistical concepts and their application in the behavioral sciences, providing clear explanations and examples.
This engaging book teaches statistical reasoning and data analysis skills through real-world examples and case studies, making it accessible for non-statisticians.
This popular textbook provides a broad overview of statistical learning methods, including supervised and unsupervised learning, and is widely used in data science and machine learning.
This engaging book presents a lively and intuitive approach to statistics, making it accessible for readers with little or no background in the subject.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/wwgbs4/p
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